Neural Radiance Field training can be accelerated through the use of grid-based representations in NeRF's learned mapping from spatial coordinates to colors and volumetric density. However, these grid-based approaches lack an explicit understanding of scale and therefore often introduce aliasing, usually in the form of jaggies or missing scene content. Anti-aliasing has previously been addressed by mip-NeRF 360, which reasons about sub-volumes along a cone rather than points along a ray, but this approach is not natively compatible with current grid-based techniques. We show how ideas from rendering and signal processing can be used to construct a technique that combines mip-NeRF 360 and grid-based models such as Instant NGP to yield error rates that are 8% - 76% lower than either prior technique, and that trains 22x faster than mip-NeRF 360.

翻译：神经辐射场训练可以通过在NeRF从空间坐标到颜色与体积密度的学习映射中采用基于网格的表示来加速。然而，这些基于网格的方法缺乏对尺度的明确理解，因此常引入锯齿伪影（通常表现为锯齿或场景内容缺失）。此前，mip-NeRF 360通过沿锥体（而非沿射线）推理子体积来解决抗锯齿问题，但该方法无法与当前基于网格的技术原生兼容。我们展示了如何利用渲染和信号处理中的思想构建一种技术，该技术结合了mip-NeRF 360与Instant NGP等基于网格的模型，使得错误率较此前任一方法降低8%-76%，且训练速度比mip-NeRF 360快22倍。